1 Data Visualization with Base R

Data visualisation important in understanding our data
Base R have a good graphing capability

1.1 Practical

generate some random number e.g. normal distribution with rnorm(_) function

Code

rand_ds <- data.frame(x = rnorm(1000, mean = 10, sd = 1), 
                      y = rnorm(1000, mean = 5, sd = 2))
rand_ds

plot scatterplot using plot(_) function

Code

plot(x = rand_ds$x, y = rand_ds$y)

2 Data Visualization with ggplot2

There are various packages that offer powerful graphing capabilities, most famous is ggplot2:: package
ggplot2 were initially developed independently, but later harmonised with tidyverse packages
based on “grammar of graphics” philosophy:
- specifying datasets,
- aesthetic mappings,
- geometric objects,
- statistical transformations,
- etc: scales, coordinate systems, and facets

Code

library(ggplot2)

ggplot(rand_ds, aes(x, y)) + geom_point()

2.1 Practical

In this practical, we will use data asthmads_clean.sav from dataset folder

Load related packages
Import dataset

Create new column, to calculate weight difference

Code

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.4
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ lubridate 1.9.3     ✔ tibble    3.2.1
✔ purrr     1.0.2     ✔ tidyr     1.3.0
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Code

library(haven)

asthmads_clean <- read_sav("asthmads_clean.sav") %>% 
  as_factor() %>% 
  mutate(Wt_Diff = Weight_Post - Weight_Pre)
asthmads_clean

With ggplot2, you begin a plot with the function ggplot(_), defining a plot object that you then add layers to.

Code

ggplot(data = asthmads_clean)

Code

asthmads_clean %>% 
  ggplot(data = .)

Next, we need to tell ggplot(_) how the information from our data will be visually represented.

The mapping argument defines how variables are mapped to visual properties (aesthetics) of the plot.
The mapping argument is always defined in the aes(_) function,
the x and y arguments of aes(_) specify which variables to map to the x and y axes.

Code

asthmads_clean %>% 
  ggplot(data = ., 
         mapping = aes(x = PA_HW, 
                       y = Wt_Diff))

Our “empty canvas” now has more structure - Physical Activity in x-axes and Weight Difference in y-axis
We then need to define a geometrical object that a plot use to represent data

in ggplot2, function start with geom_
to plot scatter plot, we use geom_point(_) function

Code

asthmads_clean %>% 
  ggplot(data = ., 
         mapping = aes(x = PA_HW, 
                       y = Wt_Diff)) + 
  geom_point()

We can add aesthetics and layers

for example, we want to layer the plot by gender
to add layer, we need to modify the aesthetic (rather than geom)

Code

asthmads_clean %>% 
  ggplot(data = ., 
         mapping = aes(x = PA_HW, 
                       y = Wt_Diff,
                       colour = Gender)) + 
  geom_point()

3 Other Charts

3.1 Bar Chart

suitable for categorical data

Code

asthmads_clean %>% 
  ggplot(aes(Gender)) + 
  geom_bar()

add colour with fill parameter

Code

asthmads_clean %>% 
  ggplot(aes(x = Gender, fill = Gender)) + 
  geom_bar()

3.2 Histogram

unlike categorical variables, numerical variables use histogram instead
commonly used to visualised the data distribution

Code

asthmads_clean %>% 
  ggplot(aes(Weight_Pre)) + 
  geom_histogram()

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

we can adjust fill to make clearer

Code

asthmads_clean %>% 
  ggplot(aes(x = Weight_Pre)) + 
  geom_histogram(fill = "white", colour = "black")

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

we can also set the bin width size

Code

asthmads_clean %>% 
  ggplot(aes(x = Weight_Pre)) + 
  geom_histogram(fill = "white", colour = "black", binwidth = 2)

3.3 Line chart (1)

Line chart usually use to visualised trend
we use newly form data in this example

Code

time_ds <- tibble(time = 1:10, 
                     value = c(2, 3, 5, 7, 8, 9, 10, 12, 14, 15))
time_ds

here, we want to plot the value by time

Code

time_ds %>% 
  ggplot(aes(x = time, y = value)) +
  geom_line()

3.4 Line chart (2)

create line chart, visualising the weight changes
however we need to wrangle, in long format

Code

asthmads_clean %>% 
  select(idR, Gender, Weight_Pre, Weight_Post) %>% 
  pivot_longer(cols = starts_with("Weight"), 
               names_to = "event",
               values_to = "weight")

add geom_point(_)
add `geom_line(_)
- add group parameter, the line “grouped” by idR

Code

asthmads_clean %>% 
  select(idR, Gender, Weight_Pre, Weight_Post) %>% 
  pivot_longer(cols = starts_with("Weight"), 
               names_to = "event",
               values_to = "weight") %>% 
  ggplot(aes(x = event,
             y = weight)) +
  geom_point() + 
  geom_line(aes(group = idR))

we can add gender as layer, adjusting the aes

Code

asthmads_clean %>% 
  select(idR, Gender, Weight_Pre, Weight_Post) %>% 
  pivot_longer(cols = starts_with("Weight"), 
               names_to = "event",
               values_to = "weight") %>% 
  ggplot(aes(x = event,
             y = weight, 
             colour = Gender)) +
  geom_point() + 
  geom_line(aes(group = idR))

4 Bonus: Malaysia Map

load the prevalence of Known DM among Malaysian (data source: NHMS 2019 report, page 36)

Code

nhms19_adm <- read_csv("../dataset/nhms19_adm.csv")

Rows: 16 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): State
dbl (3): Prevalence, Upper_CI, Lower_CI

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Code

nhms19_adm

download geojson file from dosm github

Code

download.file(
  url = "https://raw.githubusercontent.com/dosm-malaysia/data-open/main/datasets/geodata/administrative_1_state.geojson",
  destfile = "administrative_1_state.geojson",
  mode = "wb")

load sf package

Code

library(sf)

Linking to GEOS 3.11.2, GDAL 3.7.2, PROJ 9.3.0; sf_use_s2() is TRUE

harmonised (wrangling) the data
- load the map

Code

my_state_sf <- read_sf("administrative_1_state.geojson")
my_state_sf

combine prevalence dataset with map dataset

Code

left_join(nhms19_adm, my_state_sf)

adjustment maybe needed due to differing column name

Code

my_state_sf <- my_state_sf %>% 
  rename(State = state)
my_state_sf

Code

full_join(nhms19_adm, my_state_sf)

Joining with `by = join_by(State)`

the data didnt join for WP. further adjustment should be made

Code

my_state_sf <- my_state_sf %>% 
  mutate(State = fct_recode(State,
                            "WP Kuala Lumpur" = "W.P. Kuala Lumpur", 
                            "WP Putrajaya" = "W.P. Putrajaya", 
                            "WP Labuan" = "W.P. Labuan"))

nhms19_adm_m <- full_join(nhms19_adm, my_state_sf)

Joining with `by = join_by(State)`

Code

nhms19_adm_m

Plotting with geom_sf(_) function

Code

nhms19_adm_m %>% 
  ggplot(aes(fill = Prevalence)) + 
  geom_sf()

seems our dataframe is not sf object.

Code

nhms19_adm_sf <- st_as_sf(nhms19_adm_m)
nhms19_adm_sf

Plotting with geom_sf(_) function

Code

nhms19_adm_sf %>% 
  ggplot(aes(fill = Prevalence)) + 
  geom_sf()

we can also change colour to look like heat map

Code

nhms19_adm_sf %>% 
  ggplot(aes(fill = Prevalence)) + 
  geom_sf() + 
  scale_fill_gradient(low = "green", high = "red")

we can also remove the element

Code

nhms19_adm_sf %>% 
  ggplot(aes(fill = Prevalence)) + 
  geom_sf() + 
  scale_fill_gradient(low = "green", high = "red") +
  theme_bw() +
  theme(axis.text = element_blank(),
        axis.title = element_blank(),
        axis.ticks = element_blank(),
        panel.grid = element_blank(),
        panel.border = element_blank())